Assessment of Motor Impairments in Early Untreated Parkinson's Disease Patients: The Wearable Electronics Impact

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 1; pp. 120 - 130
• Main Authors: Ricci, Mariachiara, Di Lazzaro, Giulia, Pisani, Antonio, Mercuri, Nicola B., Giannini, Franco, Saggio, Giovanni
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Accelerometry - instrumentation; Adult; Aged; Aged, 80 and over; Anomalies; Atmospheric measurements; Biomedical measurement; Equipment Design; Female; Humans; IMU; Inspection; Machine Learning; Male; Middle Aged; Monitoring, Physiologic - instrumentation; motion analysis; Movement - physiology; Movement disorders; Neurodegenerative diseases; Parkinson Disease - classification; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Particle measurements; Sensorimotor integration; Sensors; signal processing; Signal Processing, Computer-Assisted - instrumentation; Task analysis; Wearable Electronic Devices; Wearable sensors; Wearable technology
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2903627

Objective: The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing objective measures of motor abnormalities. However, up to now, those sensors have been used on advanced PD patients with evident motor impairment. As a novelty, here we report the impact of wearable sensors in the evaluation of motor abnormalities in newly diagnosed, untreated, namely de novo, patients. Methods: A network of wearable sensors was used to measure motor capabilities, in 30 de novo PD patients and 30 healthy subjects, while performing five motor tasks. Measurement data were used to determine motor features useful to highlight impairments and were compared with the corresponding clinical scores. Three classifiers were used to differentiate PD from healthy subjects. Results: Motor features gathered from wearable sensors showed a high degree of significance in discriminating the early untreated de novo PD patients from the healthy subjects, with 95% accuracy. The rates of severity obtained from the measured features are partially in agreement with the clinical scores, with some highlighted, though justified, exceptions. Conclusion: Our findings support the feasibility of adopting wearable sensors in the detection of motor anomalies in early, untreated, PD patients. Significance: This work demonstrates that subtle motor impairments, occurring in de novo patients, can be evidenced by means of wearable sensors, providing clinicians with instrumental tools as suitable supports for early diagnosis, and subsequent management.